A Digital Transformation Framework for AI-Enabled Electric Vehicle Charging in Smart Cities

Abstract

As cities move towards electrification and sustainability through the increased use of electric vehicles (EV), these new modes of mobility will continue to change urban transportation. However, deploying the required EV charged infrastructure at scale across large urban centers will introduce operational, technology and energy management complexities. This study offers a comprehensive digital transformation framework for AI-based EV charging systems that increase grid reliability, support user convenience and improve energy efficiency. This comprehensive framework facilitates the integration of AI and IoT connectivity via cloud-based platforms and smart grid technologies, thus creating an intelligent and adaptive system. The research has explored problems associated with dynamic load balancing, integrating renewable energy, predictive maintenance, and estimating demand in real-time. This study used a data-driven approach to optimize the EVs, charging stations, utilities, and city infrastructures with seamless communication through digital technologies. Additionally, further, this study examined the importance of utilizing digital platforms to build scalable infrastructure, increase cyber resilience, and create a user-centric service delivery model. Using simulation-based testing, the study has shown improved EV charging efficiencies and reductions in peak electrical loads and operational costs. This model allows policymakers, city planners, and energy providers to develop resilient, sustainable, and intelligent EV charging systems. Furthermore, this framework reaffirms a larger global vision of carbon-neutral smart cities powered by digitally enabled mobility ecosystems.